RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models | Paper Citation Record

Observation a527544c-2dc5-44f4-be14-866ca39c8cbd · outbound

This paper cites GPT-4 Technical Report.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models GPT-4 Technical Report

Reference 1

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Observation 17df7105-13cc-44ee-98ba-2f66d91240cf · outbound

This paper cites A convergence analysis of gradient descent for deep linear neural networks.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models A convergence analysis of gradient descent for deep linear neural networks

Reference 2

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Observation d3e10a48-7a2b-4dcf-b898-77bdc4a75db5 · outbound

This paper cites Nonlinear programming.Journal of the Operational Research Society, 48(3):334–334, 1997.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Nonlinear programming.Journal of the Operational Research Society, 48(3):334–334, 1997

Reference 3

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Observation b97127dd-fe64-4dde-bad6-c30526d90319 · outbound

This paper cites Piqa: Reasoning about physical common- sense in natural language.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Piqa: Reasoning about physical common- sense in natural language

Reference 4

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Observation a8543f4e-9501-4d12-8bcd-cc3d3bd8a599 · outbound

This paper cites Cambridge University Press, 2023.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Cambridge University Press, 2023

Reference 5

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This paper cites Cambridge university press, 2004.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Cambridge university press, 2004

Reference 6

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Observation f4eca558-f8c6-4d84-b5f6-c76db56a0e47 · outbound

This paper cites SemEval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models SemEval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:23.420575Z

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Observation b4624a27-371f-46a4-a5e5-82de3e793c60 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Evaluating Large Language Models Trained on Code

Reference 8

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Observation 8e75e3dd-3f74-41fd-a384-c7f31bf8dbda · outbound

This paper cites On the Measure of Intelligence.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models On the Measure of Intelligence

Reference 9

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Observation 72510422-34f3-4feb-8fdd-094e834c3852 · outbound

This paper cites BoolQ: Exploring the surprising difficulty of natural yes/no questions.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models BoolQ: Exploring the surprising difficulty of natural yes/no questions

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:23.240205Z

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Observation 5b4042dd-e8b1-4ffd-a0e9-23c0e27fdcee · outbound

This paper cites Jan Maire, Leiden, 1637.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Jan Maire, Leiden, 1637

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:23.043548Z

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Observation 780c2984-885b-43b2-9dfc-025909159110 · outbound

This paper cites Qlora: Efficient finetuning of quantized llms.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Qlora: Efficient finetuning of quantized llms

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:22.899000Z

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Observation 9ba36ac6-7c72-4f32-8102-702694602f09 · outbound

This paper cites Automatically constructing a corpus of sentential paraphrases.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Automatically constructing a corpus of sentential paraphrases

Reference 13

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Observation 9ad96af1-4dd5-4317-b18b-5d44fafb64be · outbound

This paper cites Algorithmic regularization in learning deep homogeneous models: Layers are automatically balanced.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Algorithmic regularization in learning deep homogeneous models: Layers are automatically balanced

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:22.684381Z

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Observation 8b706d0c-b3b6-44bf-842f-dc6adc72589e · outbound

This paper cites Parameter- efficient fine-tuning with discrete fourier transform.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Parameter- efficient fine-tuning with discrete fourier transform

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:22.528368Z

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Observation efdb8fe9-7841-425c-a564-e14a0a3569f1 · outbound

This paper cites MIT press Cambridge, 2016.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models MIT press Cambridge, 2016

Reference 16

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Observation ffaa7396-aeed-439c-90f4-f8010d10d1b1 · outbound

This paper cites The Llama 3 Herd of Models.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models The Llama 3 Herd of Models

Reference 17

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Observation c425fb2f-8e5b-4e30-94cc-9adf9af76ceb · outbound

This paper cites Parameter-Efficient Transfer Learning with Diff Pruning.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Parameter-Efficient Transfer Learning with Diff Pruning

Reference 18

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Observation 9b8f5a68-6162-4d33-b75a-b78cc5326459 · outbound

This paper cites FLORA: Low-rank adapters are secretly gradient compres- sors.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models FLORA: Low-rank adapters are secretly gradient compres- sors

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:22.334747Z

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Observation 0784d53f-2d5c-45bc-9552-7226dc72125d · outbound

This paper cites LoRA+: Efficient low rank adaptation of large models.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models LoRA+: Efficient low rank adaptation of large models

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:22.182538Z

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Observation 96dcaa18-5495-4e43-a474-77e84690d553 · outbound

This paper cites DeBERTav3: Improving deBERTa using ELECTRA-style pre-training with gradient-disentangled embedding sharing.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models DeBERTav3: Improving deBERTa using ELECTRA-style pre-training with gradient-disentangled embedding sharing

Reference 21

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Observation 55f235b1-3920-4aee-aa4b-15be1d3bd929 · outbound

This paper cites Parameter-efficient transfer learning for NLP.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Parameter-efficient transfer learning for NLP

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:21.986716Z

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Observation ee96db6b-9fa6-4f01-83cd-7ab6b000c28c · outbound

This paper cites LoRA: Low-rank adaptation of large language models.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models LoRA: Low-rank adaptation of large language models

Reference 23

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Observation c67298aa-25e8-4c90-8311-8cfcf1b7f768 · outbound

This paper cites LLM-Adapters: An adapter family for parameter-efficient fine-tuning of large language models.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models LLM-Adapters: An adapter family for parameter-efficient fine-tuning of large language models

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:21.805509Z

Source-reported events for the cited work

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Observation 011d4730-1f16-466f-9129-eae6ba665df0 · outbound

This paper cites FedPara: Low-rank hadamard product for communication-efficient federated learning.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models FedPara: Low-rank hadamard product for communication-efficient federated learning

Reference 25

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Observation 8990b3db-2881-4fe5-a538-51c4eadfe384 · outbound

This paper cites Adam: A method for stochastic optimization.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Adam: A method for stochastic optimization

Reference 26

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Observation b9e938e7-e4c8-4b82-9d3b-be251e0f39db · outbound

This paper cites Quantum-PEFT: Ultra parameter-efficient fine-tuning.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Quantum-PEFT: Ultra parameter-efficient fine-tuning

Reference 27

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Observation 954d7b51-a8d1-4a1d-a1c7-b52d68989fab · outbound

This paper cites VeRA: Vector-based random matrix adaptation.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models VeRA: Vector-based random matrix adaptation

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:21.564535Z

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Observation 3ca259db-8ba9-490d-a8d8-a3e6c41a668b · outbound

This paper cites The power of scale for parameter-efficient prompt tuning.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models The power of scale for parameter-efficient prompt tuning

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:21.391404Z

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Observation 9db08bc5-b526-42ee-9c5e-00780593f909 · outbound

This paper cites Implicit regularization of sharpness-aware minimization for scale-invariant problems.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Implicit regularization of sharpness-aware minimization for scale-invariant problems

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:21.122174Z

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Observation 90ea32fa-4253-497d-8053-ec280b2d2d35 · outbound

This paper cites On the crucial role of initialization for matrix factorization.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models On the crucial role of initialization for matrix factorization

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:20.882603Z

Source-reported events for the cited work

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Observation 4a2c7f07-7e33-4309-95ef-4b41e75dad37 · outbound

This paper cites Geometric means.Linear algebra and its applications, 385:305–334, 2004.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Geometric means.Linear algebra and its applications, 385:305–334, 2004

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:20.703577Z

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Observation 69fa7db9-eb84-4bcd-a569-07487157f3f5 · outbound

This paper cites Prefix-tuning: Optimizing continuous prompts for generation.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Prefix-tuning: Optimizing continuous prompts for generation

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:20.448025Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation cfa51c0c-a50a-4723-a225-9174b886f9c7 · outbound

This paper cites LoftQ: LoRA-fine-tuning-aware quantization for large language models.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models LoftQ: LoRA-fine-tuning-aware quantization for large language models

Reference 34

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Observation ec8440a7-3c80-4ac4-ade4-239727e920b0 · outbound

This paper cites ReLoRA: High-rank training through low-rank updates.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models ReLoRA: High-rank training through low-rank updates

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:20.233100Z

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Observation 149affe7-bc63-488d-bbc2-b1af56bd062f · outbound

This paper cites Exploring versatile generative language model via parameter-efficient transfer learning.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Exploring versatile generative language model via parameter-efficient transfer learning

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:20.013738Z

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Observation 3850cd44-e170-4585-9935-af9e32d5b505 · outbound

This paper cites SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models SVFT: Parameter-Efficient Fine-Tuning with Singular Vectors

Reference 37

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Observation 0e78eefc-09f6-4faa-8402-957d1fee23f5 · outbound

This paper cites Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Parameter-Efficient Orthogonal Finetuning via Butterfly Factorization

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:15.853890Z

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Observation 8ec18530-d6da-487a-a3a2-0054d8530c3a · outbound

This paper cites Cola: Compute-efficient pre-training of llms via low-rank activation.arXiv preprint arXiv:2502.10940, 2025.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Cola: Compute-efficient pre-training of llms via low-rank activation.arXiv preprint arXiv:2502.10940, 2025

Reference 39

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Observation 61785207-2724-4dbf-b127-dbac7298f76c · outbound

This paper cites Decoupled weight decay regularization.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Decoupled weight decay regularization

Reference 40

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Observation 66d7b25d-149f-41e4-8f0a-4897fbfb3128 · outbound

This paper cites Pissa: Principal singular values and singular vectors adap- tation of large language models.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Pissa: Principal singular values and singular vectors adap- tation of large language models

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:19.900030Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 1a7e628e-12dc-4779-8dfb-70487c6e64db · outbound

This paper cites Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:15.889901Z

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Observation 79498881-79f0-4e2a-89d7-129f7491f198 · outbound

This paper cites Learning on LoRAs: GL-Equivariant Processing of Low-Rank Weight Spaces for Large Finetuned Models.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Learning on LoRAs: GL-Equivariant Processing of Low-Rank Weight Spaces for Large Finetuned Models

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:15.903127Z

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Observation 45f55186-d930-4760-935d-fdb81b60060d · outbound

This paper cites Know what you don’t know: Unanswerable questions for SQuAD.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Know what you don’t know: Unanswerable questions for SQuAD

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:19.777479Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation b115734a-7935-403b-ac52-a434c251912d · outbound

This paper cites High-resolution image synthesis with latent diffusion models.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models High-resolution image synthesis with latent diffusion models

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:19.682501Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 7e5c48db-e302-4ede-8b59-7bea01c174db · outbound

This paper cites AdapterDrop: On the efficiency of adapters in transformers.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models AdapterDrop: On the efficiency of adapters in transformers

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:19.565489Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation c8ae42bb-06eb-46b5-abd9-a652af2670e6 · outbound

This paper cites McGraw-Hill, New York, 3rd edition, 1976.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models McGraw-Hill, New York, 3rd edition, 1976

Reference 47

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Observation bc981061-040f-443e-a6c7-be004b13d0f8 · outbound

This paper cites Dream- booth: Fine tuning text-to-image diffusion models for subject-driven generation.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Dream- booth: Fine tuning text-to-image diffusion models for subject-driven generation

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:19.419673Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 085149b8-52f8-4660-8dfb-496d39b116f6 · outbound

This paper cites Winogrande: An adversarial winograd schema challenge at scale.Communications of the ACM, 64(9):99–106, 2021.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Winogrande: An adversarial winograd schema challenge at scale.Communications of the ACM, 64(9):99–106, 2021

Reference 49

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Observation 048d6765-dd50-4d45-aac2-c38bc65d1ff8 · outbound

This paper cites SocialIQA: Commonsense Reasoning about Social Interactions.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models SocialIQA: Commonsense Reasoning about Social Interactions

Reference 50

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:16.004228Z

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Observation fa604ac7-b69e-4472-b550-72bfd21e993e · outbound

This paper cites Ge- oloRA: Geometric integration for parameter efficient fine-tuning.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Ge- oloRA: Geometric integration for parameter efficient fine-tuning

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:19.237272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 165fd47c-539b-4985-961e-0c54b26049b5 · outbound

This paper cites Cambridge university press, 2014.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Cambridge university press, 2014

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:16.023260Z

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Observation dceb3860-b33f-4969-a5eb-94541ea6fd7c · outbound

This paper cites Recursive deep models for semantic compositionality over a sentiment treebank.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Recursive deep models for semantic compositionality over a sentiment treebank

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:19.110548Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 848506c5-9588-4309-8803-c489d91d19b2 · outbound

This paper cites Training neural networks with fixed sparse masks.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Training neural networks with fixed sparse masks

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:18.948854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ce1c2f75-d5c3-4734-afc8-e6f347e3ae73 · outbound

This paper cites Galactica: A Large Language Model for Science.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Galactica: A Large Language Model for Science

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:16.047793Z

Source-reported events for the cited work

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Observation 28615673-0aeb-4843-9a2a-b17c237fff93 · outbound

This paper cites Accelerating ill-conditioned low-rank matrix estimation via scaled gradient descent.J.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Accelerating ill-conditioned low-rank matrix estimation via scaled gradient descent.J

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:18.786532Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation ca9396cc-f70b-405a-9486-e97c4777c470 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models LLaMA: Open and Efficient Foundation Language Models

Reference 57

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:16.066259Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cb3ae27-d87b-47d2-918c-3ac307ee099c · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 58

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:16.081344Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 23f182ac-72a6-4d49-9615-2d81ac6269fc · outbound

This paper cites GLUE: A multi-task benchmark and analysis platform for natural language understanding.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models GLUE: A multi-task benchmark and analysis platform for natural language understanding

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:18.593026Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 263824d8-d999-43b8-946b-efd3fa874c7d · outbound

This paper cites Lora-ga: Low-rank adaptation with gradient approximation.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Lora-ga: Low-rank adaptation with gradient approximation

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:18.459699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 628b48d0-ac1a-4c45-bc0e-84c85ef50d04 · outbound

This paper cites LoRA-pro: Are low-rank adapters properly optimized? InProc.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models LoRA-pro: Are low-rank adapters properly optimized? InProc

Reference 61

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:16.110697Z

Source-reported events for the cited work

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Observation f97d534e-53ec-471c-963c-9dd0a3d301ba · outbound

This paper cites Neural network acceptability judgments.Trans.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Neural network acceptability judgments.Trans

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:18.253990Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 4e1d806c-b054-48fd-87ce-98bb164de3d8 · outbound

This paper cites A broad-coverage challenge corpus for sentence understanding through inference.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models A broad-coverage challenge corpus for sentence understanding through inference

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:18.032194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 57c49d2a-c882-42fc-88a6-d524e00ddc16 · outbound

This paper cites Reft: Representation finetuning for language models.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Reft: Representation finetuning for language models

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:17.885492Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation f0229c86-8f6b-4deb-8833-32743bb8c0f8 · outbound

This paper cites DoRA: Weight-decomposed low-rank adaptation.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models DoRA: Weight-decomposed low-rank adaptation

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:17.715773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 95dee3c0-d29b-4039-8d41-e50f4d501659 · outbound

This paper cites Navigating text-to-image customization: From LyCORIS fine-tuning to model evaluation.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Navigating text-to-image customization: From LyCORIS fine-tuning to model evaluation

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:17.552027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation dabc406a-828c-432f-ad3f-815560b0e73a · outbound

This paper cites LoRA done RITE: Robust invariant transformation equilibration for loRA optimization.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models LoRA done RITE: Robust invariant transformation equilibration for loRA optimization

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:16.165945Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bb47905-49dc-4ba2-b6e4-6c530a36562a · outbound

This paper cites HellaSwag: Can a Machine Really Finish Your Sentence?.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models HellaSwag: Can a Machine Really Finish Your Sentence?

Reference 68

Resolution: unresolved; no resolver link, observed 2026-08-07T14:28:16.172157Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9059868f-1951-4555-a7ac-48e31abff9bb · outbound

This paper cites Riemannian preconditioned LoRA for fine-tuning foundation models.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Riemannian preconditioned LoRA for fine-tuning foundation models

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:17.358479Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 689cc362-a8f6-4c9e-b460-78bfc93e1b1b · outbound

This paper cites Limitations.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Limitations

Reference 70

Resolution: malformed identifier; raw_fallback, observed 2026-08-07T14:28:17.144327Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

Observation 89ed4b99-1755-4c21-b8e0-3097e0a1bd48 · outbound

This paper cites Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.

RefLoRA: Refactored Low-Rank Adaptation for Efficient Fine-Tuning of Large Models Guidelines: • The answer NA means that the paper does not involve crowdsourcing nor research with human subjects

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T14:28:16.944188Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

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